Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
BananaGroup now runs its Medicare Supplement sales on its own app, with prospects, quotes, enrollment alerts and the sales pipeline in one place, built in a couple of days. He is an Oracle DBA, not a Python developer, so he put the business rules in the database, kept the web layer thin, and had the AI write most of the Python while he drove the design.
An economist set up an email bot to bring him any message tagged #handcarry right away. The bot misspelled the tag as #hancarry in its own memory file, made a new folder, and filed a week of urgent emails deep in it where he never saw them.
The AI automated porting business software from QuickBASIC on DOS to Tcl on Linux, usually right first try, to add features management needed.
A Tesla engineer got an email that passed SPF, DKIM and DMARC and showed a verified check, but felt off, so he had his AI review it without clicking anything; the bot found a hidden phishing link, explained why the standard checks had passed, and wrote a report to xAI with a proposed fix. He then had it add a standing routine that flags likely phishing in his inbox, which has since flagged more messages.
A creator who could not afford an editor paid for the AI Max and built his own post production studio, about 31,000 lines of code. Its smart cutting takes his raw filming and builds a DaVinci Resolve timeline for him, saving about 95% of the cutting time, and it also generates shorts and uploads with titles, descriptions, chapters and tags.
Josh Collier ('Fractional AI Officer', 7.6K followers) showed the AI connected to Notion pulling his entire archive of 2,556 saved FB/IG items into a 'Saved Library' database with category, platform, type, author, link, hashtags, date, and full caption text. Breakdown read aloud: Exercise 562, Business 231, Mindset 221, Finance 144, Golf 137, Faith 97, AI 91, plus Lifestyle/Tech/Health and ~1,000 older captionless saves in 'Other.' He called it his AI now 'knowing' everything he saved.
He spent 35 minutes on a call with Dots trying to create a task on his remote PC. Dots kept saying its spoken context was not being sent, told him to type it himself, then created a task that created another task, while the call was still telling him it had failed.
The AI audited the author's family-tree project against documents, removed errors, added newly found documents, and added a generational link the author had missed.
Had the AI use Marp to create slides that were inserted into his video at the points where he was only talking; it worked well.
The AI filled out his car insurance application for him. Where an answer was not in what it knew, it stopped and asked him instead of guessing.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.